Multiagent planning is concerned with planning by (and for) multiple agents. It can involve agents planning for a common goal, an agent coordinating the plans or planning of others, or agents refining their own plans while negotiating over tasks or resources. Potential applications can be found in manufacturing, space exploration, and military operations, to name a few. This tutorial will identify and define variations of the multiagent planning problem, discuss issues in designing a multiagent planning system, and describe some real-world multiagent planning problems. We will review research contributions to this sub-field and describe frameworks and systems such as Distributed NOAH, GPGP, and DSIPE. In addition, we will describe multiagent planning's relation to other fields and opportunities for new research. Basic knowledge of artificial intelligence and planning techniques will be helpful.
Brad Clement is a senior member of the Artificial Intelligence Group at the Jet Propulsion Laboratory in Pasadena, CA, where he is developing methods for coordinating planning and scheduling for single and multiple spacecraft/missions. He received a bachelor degree in computer engineering from the Georgia Institute of Technology and M.S. and Ph.D. degrees in computer science and engineering from the University of Michigan, Ann Arbor. His research interests include multiagent coordination, situated planning and execution, distributed systems, and AI in games.